10+ Mehrotra & Mehrotra Interview Questions and Answers

Returning purchased goods will decrease inventory and accounts payable on the balance sheet.

• Returning purchased goods will decrease the inventory account on the balance sheet.

• Accounts payable will also decrease as the company will no longer owe the supplier for the returned goods.

• The decrease in inventory and accounts payable will result in a decrease in total assets and liabilities on the balance sheet.

• The impact on the balance sheet will depend on the value of the returned ...read more

Depreciation is the decrease in value of an asset over time. Furniture and fixture have a depreciation rate and method of charged depreciation.

• Depreciation is a non-cash expense that reflects the wear and tear of an asset over time

• Furniture and fixture have a useful life and a salvage value

• The most common method of charged depreciation is the straight-line method

• The depreciation rate and method of charged depreciation may vary depending on the company's accounting policies an...read more

Closing stock is shown on the assets side of the trial balance.

• Closing stock is an asset and is shown on the assets side of the trial balance.

• It is usually shown under the inventory or stock-in-hand account.

• The value of closing stock is calculated by taking the physical inventory count and multiplying it by the cost per unit.

• The closing stock figure is carried forward to the next accounting period as the opening stock.

GST was imposed by the government to simplify the tax system and increase revenue.

• To simplify the tax system by replacing multiple indirect taxes with a single tax

• To increase revenue by widening the tax base and reducing tax evasion

• To promote ease of doing business by reducing compliance burden

• To make Indian goods competitive in the global market by providing input tax credit

• Examples: GST replaced taxes like VAT, excise duty, service tax, etc.

• GST has led to an increase in tax...read more

Overfitting occurs when a model learns the training data too well, leading to poor generalization. Underfitting occurs when a model is too simple to capture the underlying patterns in the data.

• Overfitting: model performs well on training data but poorly on unseen data

• Underfitting: model is too simple and fails to capture the underlying patterns in the data

• Overfitting can be addressed by using techniques like regularization, cross-validation, and early stopping

• Underfitting can...read more

To remove duplicates from a string, we can use a set data structure to store unique characters.

• Create an empty set to store unique characters.

• Iterate through each character in the string.

• Add each character to the set if it's not already present.

• Join the characters in the set to form the final string without duplicates.

Stack, queue, and BST are data structures used in computer science for storing and organizing data.

• Stack: Last in, first out data structure. Examples include undo functionality in text editors.

• Queue: First in, first out data structure. Examples include printer queues.

• BST (Binary Search Tree): Hierarchical data structure where each node has at most two children. Used for efficient searching and sorting.

An object is an instance of a class, which is a blueprint for creating objects.

• An object is a specific instance of a class that contains data and behavior.

• A class is a blueprint or template for creating objects.

• An instance is a single occurrence of an object created based on a class.

MSE stands for Mean Squared Error, a common metric used to measure the average squared difference between predicted values and actual values.

• MSE is calculated by taking the average of the squared differences between predicted and actual values.

• It is commonly used in machine learning to evaluate the performance of regression models.

• Lower MSE values indicate better model performance, as they represent smaller errors between predicted and actual values.

• Example: If actual values ...read more

Filters in CNN are small matrices used to extract features from input data by performing convolution operations.

• Filters are applied to small regions of the input data to detect specific patterns or features.

• Each filter slides over the input data and performs element-wise multiplication followed by summation to produce a feature map.

• Filters are learned during the training process to capture important features for the task at hand.

• Common filter sizes are 3x3 or 5x5, and multipl...read more